ArticleIEEE journal of biomedical and health informatics2018
Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis.
Article in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.BioMed research international · 2022Pooled it
- Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.eLife · 2024Article
- Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.Research square · 2024Article
- Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Dysfunctions of multiscale dynamic brain functional networks in subjective cognitive decline.Brain communications · 2024Article
- Deep joint learning of pathological region localization and Alzheimer's disease diagnosis.Scientific reports · 2023Article
- An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data.IEEE transactions on pattern analysis and machine intelligence · 2022Article
- Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer's Disease Diagnosis.Frontiers in aging neuroscience · 2022Article
- A deep look into radiomics.La Radiologia medica · 2021Review
- Learning rich features with hybrid loss for brain tumor segmentation.BMC medical informatics and decision making · 2021Article
- THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer's disease.Quantitative imaging in medicine and surgery · 2021Article
- Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.Journal of digital imaging · 2020Article
- Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages.IEEE transactions on medical imaging · 2020Article
- Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.IEEE transactions on bio-medical engineering · 2020Article
- Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.IEEE transactions on medical imaging · 2020Article
- Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks.PloS one · 2020Article
- Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs.Frontiers in cardiovascular medicine · 2020Article
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5 authors.
Funding
Abstract
Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer's disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer's disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer's disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.